Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00268
Ali Saadat, E. Masehian
Cloud computing systems play a vital role in the digital age. A critical bottleneck in most scenarios in cloud computing is the high degree of unpredictability with respect to resource availability and network bandwidth, which may lead to low Quality of Service (like low response times), which can be improved by Load Balancing. Load balancing concerns with efficiently distributing incoming network traffic across a group of servers. This ensures no single server bears too much demand, and thus the availability of applications and websites for users is increased. Due to the huge state-space of such a problem, implementing task scheduling algorithms in load balancing can be very effective. In this paper, we propose a hybrid intelligent approach to load balancing: a Genetic Algorithm module arranges the jobs randomly, and a fuzzy logic module builds the objective function for determining busy states of servers according to their RAM and CPU task queues. The fuzzy input variables include the satisfaction degree and the start and end times of the service, and the fuzzy output is service availability. Computational experiments showed that the best solution was obtained within half of the planned execution time, which leads to higher user satisfaction degree.
{"title":"Load Balancing in Cloud Computing Using Genetic Algorithm and Fuzzy Logic","authors":"Ali Saadat, E. Masehian","doi":"10.1109/CSCI49370.2019.00268","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00268","url":null,"abstract":"Cloud computing systems play a vital role in the digital age. A critical bottleneck in most scenarios in cloud computing is the high degree of unpredictability with respect to resource availability and network bandwidth, which may lead to low Quality of Service (like low response times), which can be improved by Load Balancing. Load balancing concerns with efficiently distributing incoming network traffic across a group of servers. This ensures no single server bears too much demand, and thus the availability of applications and websites for users is increased. Due to the huge state-space of such a problem, implementing task scheduling algorithms in load balancing can be very effective. In this paper, we propose a hybrid intelligent approach to load balancing: a Genetic Algorithm module arranges the jobs randomly, and a fuzzy logic module builds the objective function for determining busy states of servers according to their RAM and CPU task queues. The fuzzy input variables include the satisfaction degree and the start and end times of the service, and the fuzzy output is service availability. Computational experiments showed that the best solution was obtained within half of the planned execution time, which leads to higher user satisfaction degree.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115342550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00165
Srivalli Dingari, N. Mahapatra
Choosing the most suitable college courses can be a time-consuming task, given the number of sources from which students need to pull the information regarding degree requirements. In addition, given the limited time and interaction between advisor and student, substantial effort needs to be put in to find a proper path towards graduation. To bridge the gap, a number of degree auditing software systems emerged and evolved, making it easier for students to have a convenient road map and plan their graduation. This study surveys the features of popular degree auditing systems and two research papers, one from Cornell University and the other from Texas State University, on the design and structure of a degree auditing system.
{"title":"A Short Survey of Degree Auditing Systems","authors":"Srivalli Dingari, N. Mahapatra","doi":"10.1109/CSCI49370.2019.00165","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00165","url":null,"abstract":"Choosing the most suitable college courses can be a time-consuming task, given the number of sources from which students need to pull the information regarding degree requirements. In addition, given the limited time and interaction between advisor and student, substantial effort needs to be put in to find a proper path towards graduation. To bridge the gap, a number of degree auditing software systems emerged and evolved, making it easier for students to have a convenient road map and plan their graduation. This study surveys the features of popular degree auditing systems and two research papers, one from Cornell University and the other from Texas State University, on the design and structure of a degree auditing system.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121178471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00298
Dong h. Lee, Kong-Rae Lee, J. H. Lee
we will introduce a new program so-called TVA(Tech-Venture Academy) for the setup of a role model, and cultivation of outstanding enterprise innovation experts and investigate the performance of its program to overcome the crisis faced by the Korean manufacturing industry due to the global economic recession and the dumping of companies in developing countries, and to lead the role of regional industry promotion and also, to introduce innovation management experts and industry re-creation because DGIST should play as a science and technology specialization and leading university located in Daegu of South Korea.
{"title":"Development of Innovative Education Program for Tech-Oriented Industrial Structure Improvement of Local Industries by Fostering Start-Up Companies: TVA (Tech-Venture Academy) Program","authors":"Dong h. Lee, Kong-Rae Lee, J. H. Lee","doi":"10.1109/CSCI49370.2019.00298","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00298","url":null,"abstract":"we will introduce a new program so-called TVA(Tech-Venture Academy) for the setup of a role model, and cultivation of outstanding enterprise innovation experts and investigate the performance of its program to overcome the crisis faced by the Korean manufacturing industry due to the global economic recession and the dumping of companies in developing countries, and to lead the role of regional industry promotion and also, to introduce innovation management experts and industry re-creation because DGIST should play as a science and technology specialization and leading university located in Daegu of South Korea.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124587353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00107
Johanna Hopane, B. Gatsheni
Flight departure delays are a major problem at OR Tambo International airport (ORTIA) located in Johannesburg in South Africa. These delays are more pronounced at the beginning and end of the month. Flight delays at ORTIA do impact negatively on business, on job opportunities and on tourists. Machine learning algorithms namely Decision Trees (J48), Support Vector Machine (SVM), K-Means Clustering (K-Means) and Multi Layered Perceptron (MLP) were used to construct the flight departure delays prediction models. Cross-validation (CV) was used for evaluating the models. The best prediction model was selected by using a confusion matrix and the ROC curve. The results show that the models constructed using data and the Decision Trees is suited for flight departure delay prediction as it gave the best prediction of 67.144%. The implications of the model is that travellers wishing to travel from ORTIA can foretell the flight departure delays using the tool. The tool will allow the travellers to enter variables such as month, week of month, day of week and time of day.
{"title":"A Computational Intelligence-Based Prediction Model for Flight Departure Delays","authors":"Johanna Hopane, B. Gatsheni","doi":"10.1109/CSCI49370.2019.00107","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00107","url":null,"abstract":"Flight departure delays are a major problem at OR Tambo International airport (ORTIA) located in Johannesburg in South Africa. These delays are more pronounced at the beginning and end of the month. Flight delays at ORTIA do impact negatively on business, on job opportunities and on tourists. Machine learning algorithms namely Decision Trees (J48), Support Vector Machine (SVM), K-Means Clustering (K-Means) and Multi Layered Perceptron (MLP) were used to construct the flight departure delays prediction models. Cross-validation (CV) was used for evaluating the models. The best prediction model was selected by using a confusion matrix and the ROC curve. The results show that the models constructed using data and the Decision Trees is suited for flight departure delay prediction as it gave the best prediction of 67.144%. The implications of the model is that travellers wishing to travel from ORTIA can foretell the flight departure delays using the tool. The tool will allow the travellers to enter variables such as month, week of month, day of week and time of day.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129832152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00238
Ananth N. Ramaseri Chandra, Fatima El Jamiy, H. Reza
Information delivery in a visual format is always a better way of communication. Even with many data visualization techniques available, visualizing enormous amounts of data has always been a challenge. With recent advancements in technology, many new visualization techniques unfold, one of which is visualizing data through Augmented reality(AR). AR and big data have always gone together as AR requires large data sets to render information virtually in a real-time environment, and big data provides the same. In this paper, we explore some of the conventional visualization techniques and discuss the scope and possibilities for AR data visualizations. We also explore the areas implementing the technique of visualizing big data with AR. The advantages and limitations are also discussed.
{"title":"Augmented Reality for Big Data Visualization: A Review","authors":"Ananth N. Ramaseri Chandra, Fatima El Jamiy, H. Reza","doi":"10.1109/CSCI49370.2019.00238","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00238","url":null,"abstract":"Information delivery in a visual format is always a better way of communication. Even with many data visualization techniques available, visualizing enormous amounts of data has always been a challenge. With recent advancements in technology, many new visualization techniques unfold, one of which is visualizing data through Augmented reality(AR). AR and big data have always gone together as AR requires large data sets to render information virtually in a real-time environment, and big data provides the same. In this paper, we explore some of the conventional visualization techniques and discuss the scope and possibilities for AR data visualizations. We also explore the areas implementing the technique of visualizing big data with AR. The advantages and limitations are also discussed.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131157778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00128
P. Acevedo, M. Vazquez
In this work tumor classification was performed using K-means and GLCM algorithms to segment ultrasound images. In order to apply Stavros criteria, a lineal support vector machine (SVM) algorithm was used to classify benign and malignant tumors. 94% of echographies were correctly classified.
{"title":"Classification of Tumors in Breast Echography Using a SVM Algorithm","authors":"P. Acevedo, M. Vazquez","doi":"10.1109/CSCI49370.2019.00128","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00128","url":null,"abstract":"In this work tumor classification was performed using K-means and GLCM algorithms to segment ultrasound images. In order to apply Stavros criteria, a lineal support vector machine (SVM) algorithm was used to classify benign and malignant tumors. 94% of echographies were correctly classified.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128679603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00070
Pablo Rivas, Chelsi Chelsi, Nishit Nishit, Laharika Ravula
Advances in machine learning are making possible the interaction between humans and machines, coming closer to passing the Turing test. Chatbots, specifically, are a technology that uses the latest advances in natural language processing and machine learning to understand text and produce text in response to input. While this is an important achievement today, we must consider specific challenges that chatbot deployments might pose. This paper looks back to a historical event that took place in 2016 with the purpose of extracting important, memorable, lessons. The study suggests that certain assumptions with respect to societal values are of paramount importance and need to be considered carefully along with a proper platform selection.
{"title":"Application-Agnostic Chatbot Deployment Considerations: A Case Study","authors":"Pablo Rivas, Chelsi Chelsi, Nishit Nishit, Laharika Ravula","doi":"10.1109/CSCI49370.2019.00070","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00070","url":null,"abstract":"Advances in machine learning are making possible the interaction between humans and machines, coming closer to passing the Turing test. Chatbots, specifically, are a technology that uses the latest advances in natural language processing and machine learning to understand text and produce text in response to input. While this is an important achievement today, we must consider specific challenges that chatbot deployments might pose. This paper looks back to a historical event that took place in 2016 with the purpose of extracting important, memorable, lessons. The study suggests that certain assumptions with respect to societal values are of paramount importance and need to be considered carefully along with a proper platform selection.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128837658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00076
N. Rowe, Charles Knight
Simulations can produce large quantities of data. To reason about the results of simulations, machine-learning methods can be helpful. We explored a case-based reasoning approach to summarizing the results of a probabilistic simulation of naval combat involving missiles. We used a tree structure to index the data and showed that it gave good accuracy in estimating the results of this simulation with new parameters. We are now extending these ideas to a more complex military simulation.
{"title":"Case-Based Reasoning for Summarizing Simulation Results","authors":"N. Rowe, Charles Knight","doi":"10.1109/CSCI49370.2019.00076","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00076","url":null,"abstract":"Simulations can produce large quantities of data. To reason about the results of simulations, machine-learning methods can be helpful. We explored a case-based reasoning approach to summarizing the results of a probabilistic simulation of naval combat involving missiles. We used a tree structure to index the data and showed that it gave good accuracy in estimating the results of this simulation with new parameters. We are now extending these ideas to a more complex military simulation.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128843800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00071
Ivan Ortiz Garcés, María Cazares, R. Andrade
The number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.
{"title":"Detection of Phishing Attacks with Machine Learning Techniques in Cognitive Security Architecture","authors":"Ivan Ortiz Garcés, María Cazares, R. Andrade","doi":"10.1109/CSCI49370.2019.00071","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00071","url":null,"abstract":"The number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125904851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/CSCI49370.2019.00239
Yuan-Yuan Lee, Y. Chang
Consumer behavior analytics is at the epicenter of a Big Data revolution. In this paper we propose to analyze intra-regional spatial patterns mining tourists' behaviors and characteristics based on traveling group size with data collected from Airbnb open source focused on Los Angeles neighborhood in 2016. Random Forest Classification (RF) technique, an ensemble approach, is applied to identify the key drivers according to relevant traveler groups and presented patterns using Hotspot Analysis on Geographic Information System (GIS). Our empirical result highlights driving factors within Airbnb listings, providing valuable insights to better plan, monitor and manage tourism activity.
{"title":"Uncovering Los Angeles Tourists' Patterns Using Geospatial Analysis and Supervised Machine Learning with Random Forest Predictors","authors":"Yuan-Yuan Lee, Y. Chang","doi":"10.1109/CSCI49370.2019.00239","DOIUrl":"https://doi.org/10.1109/CSCI49370.2019.00239","url":null,"abstract":"Consumer behavior analytics is at the epicenter of a Big Data revolution. In this paper we propose to analyze intra-regional spatial patterns mining tourists' behaviors and characteristics based on traveling group size with data collected from Airbnb open source focused on Los Angeles neighborhood in 2016. Random Forest Classification (RF) technique, an ensemble approach, is applied to identify the key drivers according to relevant traveler groups and presented patterns using Hotspot Analysis on Geographic Information System (GIS). Our empirical result highlights driving factors within Airbnb listings, providing valuable insights to better plan, monitor and manage tourism activity.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"18 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114031365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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